CN110019545A - The processing of industrial data and analysis method, the processing unit of industrial data and data warehouse - Google Patents
The processing of industrial data and analysis method, the processing unit of industrial data and data warehouse Download PDFInfo
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- 238000004458 analytical method Methods 0.000 title claims abstract description 37
- 238000012545 processing Methods 0.000 title claims abstract description 28
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- 238000007405 data analysis Methods 0.000 claims abstract description 67
- 238000013499 data model Methods 0.000 claims abstract description 63
- 238000000034 method Methods 0.000 claims abstract description 35
- 238000003672 processing method Methods 0.000 claims abstract description 5
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
Abstract
This application provides a kind of processing method and processing devices of industrial data, and the data for belonging to production factors classification are extracted from industrial data, and the data model of production factors classification is established based on the data extracted.Present invention also provides a kind of industrial data analysis method and data warehouses, based on the data model of production factors classification, the analysis of data analysis requirements is provided a user as a result, therefore, big data analysis can be carried out to industrial data, meet the demand of the industrial data analysis of user.
Description
Technical field
This application involves the processing of electronic information field more particularly to a kind of industrial data and analysis methods, industrial data
Processing unit and data warehouse.
Background technique
Currently, the Various types of data of industrial production industry is dispersed in each information system, such as file system, service interface
Deng.These information systems often do not have big data computing capability.
Therefore, the duct type material conveying based on industry, continuous production be strong, standard process, craft flexibility are small, product list
One and the stable feature of raw material, how big data processing capacity is utilized, a kind of data-handling capacity of industrial data is provided, with right
Industrial manufacturing process analyzed, process optimization or the monitoring of industry manufacture etc., is a kind of trend.
But there is presently no a kind of big data processing schemes suitable for industrial data.
Summary of the invention
This application provides a kind of processing of industrial data and analysis methods, the processing unit of industrial data and data bins
Library is, and it is an object of the present invention to provide a kind of processing and analytical technology suitable for industrial data.
To achieve the goals above, this application provides following technical schemes:
A kind of processing method of industrial data, comprising:
Obtain industrial data to be processed；
The data for belonging to multiple production factors classifications are extracted from the industrial data, wherein the production factors classification
Refer to the corresponding classification of production factors needed for producing one production process of a product or operation in industrial process stream；
Based on extract obtain described in belong to the data of multiple production factors classifications, establish the number of multiple production factors classifications
According to model, so that the data model based on foundation analyzes analysis demand required for user.
Optionally, each production needed for one production process of a product or operation is produced in the industrial process stream
Classification belonging to element includes:
Produce a product or operation one production process, the people being related to, machine, material, method, ring, energy, give up in one or
It is multiple.
Optionally, acquisition industrial data to be processed includes:
The industrial data that the data source exports is obtained from data source；
Filtering rule based on setting is filtered processing to the industrial data, obtains the industrial number to be processed
According to.
Optionally, before the data model for establishing each production factors classification, further includes:
The data for belonging to same production factors classification are unified for the data of preset format.
A kind of industrial data analysis method, comprising:
Data analysis requirements are obtained, the data analysis requirements include data type to be analyzed；
Based on the data type to be analyzed, obtained from the data model of the multiple production factors classifications pre-established
Associated data；
The associated data of acquisition are analyzed, and obtained analysis result is supplied to user；
Wherein, the production factors classification refers to that a product or operation one are produced in industrial process stream to be produced
The corresponding classification of the production factors of Cheng Suoxu；The data model of the production factors classification is based on the category extracted from industrial data
It is determined in the data of production factors classification.
Optionally, the acquisition data analysis requirements include:
The data analysis requirements for receiving user's input, alternatively, pre-establishing the production factors according to described
The data model and the corresponding data analysis requirements of industry of classification determine that the corresponding data analysis of the data model needs
It asks.
A kind of processing unit of industrial data, comprising:
Module is obtained, for obtaining industrial data to be processed；
Abstraction module, for extracting the data for belonging to multiple production factors classifications from the industrial data, wherein described
Production factors classification refers to production factors needed for one production process of one product of production or operation in industrial process stream
Corresponding classification；
Establish module, for based on extract obtain described in belong to the data of multiple production factors classifications, establish multiple lifes
The data model of feature category is produced, so that the data model based on foundation divides analysis demand required for user
Analysis.
Optionally, production needed for one production process of a product or operation is produced in the industrial process stream is wanted
The multiple classification of element includes:
Produce a product or operation one production process, the people being related to, machine, material, method, ring, energy, give up in one or
It is multiple.
A kind of data warehouse, comprising:
First obtains module, and for obtaining data analysis requirements, the data analysis requirements include data class to be analyzed
Type；
Second obtains module, for based on the data type to be analyzed, from the multiple production factors classes pre-established
Associated data are obtained in other data model；
Analysis module for analyzing the associated data of acquisition, and obtained analysis result is provided
To user；
Wherein, the production factors classification refers to that a product or operation one are produced in industrial process stream to be produced
The corresponding classification of the production factors of Cheng Suoxu；The data model of the production factors classification is based on extracting from the industrial data
Belong to production factors classification data determine.
Optionally, the first acquisition module is specifically used for:
The data analysis requirements for receiving user's input, alternatively, pre-establishing the production factors according to described
The data model and the corresponding data analysis requirements of industry of classification determine that the corresponding data analysis of the data model needs
It asks.
The processing method and processing device of industrial data described herein extracts from industrial data and belongs to production factors class
Other data, and establish based on the data extracted the data model of production factors classification.Industrial data described herein point
Analysis method and data warehouse provide a user the analysis knot of data analysis requirements based on the data model of production factors classification
Therefore fruit can carry out big data analysis to industrial data, meet the demand of the industrial data analysis of user.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of application for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is the architecture diagram of data warehouse disclosed in the embodiment of the present application；
Fig. 2 is the structural schematic diagram of data warehouse disclosed in the embodiment of the present application；
Fig. 3 is the example flow diagram of industrial data analysis method disclosed in the embodiment of the present application；
Fig. 4 is the structural schematic diagram of the processing unit of industrial data disclosed in the embodiment of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.It is based on
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall in the protection scope of this application.
It is DSS and on-line analysis application number in fact since data warehouse is the basis for carrying out big data processing
According to the structural data environment in source.Data warehouse is characterized in that subject-oriented, integration, stability and time variation.The application
Using these features of data warehouse, industrial big data can be handled, to promote the processing capacity of industrial big data.Tool
For body, the application is established by the analysis to industrial big data with object-based data processing method, that is, based on life
It produces feature category and establishes data model, so as to carrying out certain processing to the mass data generated in industry, so that with
Family can carry out data analysis etc. based on the data model of the foundation, can provide data analysis capabilities for industry manufacture, such as
The report of industry manufacture, real time information of industrial monitoring, etc..
Fig. 1 is a kind of data warehouse disclosed in the embodiment of the present application, including five-layer structure, is respectively as follows: temporary layer, operation
Layer, summarizes layer and application layer at detail layer.In the present embodiment, described " layer " is equivalent to " module ".
Data warehouse shown in FIG. 1 can be set in cloud platform, based on the computing capability of cloud platform to data at
Reason.
Below by taking the Flow Manufacturing in industry as an example, this five layers data processing function will be carried out specifically respectively
It is bright.
1, temporary layer is used to obtain data from data source, the raw material data as data warehouse.
Specifically, as shown in Figure 1, data source includes but is not limited to the database of Flow Manufacturing, each information system, sets
Standby interface and open protocol connect (open protocol connection, OPC) equipment.In general, each data source
Data are stored in the form of tables of data, so, the raw material data that temporary layer obtains can be each tables of data.
2, operation layer is used for the professional standard according to Flow Manufacturing, and filtering does not meet the data of professional standard.
Professional standard can be existing agreement or process, run specification of the layer based on data in professional standard, delete
Some data for not meeting professional standard.
Specifically, as shown in Figure 1, professional standard includes but is not limited to buying, formula, production, quality inspection and sale aspect
Standard.
3, detail layer is used for each production factors classification according to Flow Manufacturing, the data after being cleaned using operation layer,
Establish data model.
The production factors classification of Flow Manufacturing refers to, one production of a product or operation is produced in Flow Manufacturing
Process, the required corresponding classification of each production factors, specifically includes: (production factors for belonging to the category include by the people of participation
Employee's basic condition, department, production changing shifts, employee's title for technical personnel rank), the machine that uses or tool (belong to the life of the category
Producing element includes equipment basic condition, device type, produces and uses situation, and disorderly closedown situation maintains situation), use
Raw material (production factors for belonging to the category include purchase channel, logistics transportation, supplied materials detection, permit standard), use
The energy of process flow or commercial run, the workshop condition of needs or facility environment, needs (belongs to the production factors example of the category
Such as water, electricity, gas, network) and be related to waste material (belong to the category production factors include generate, store, declaring, shifting,
Disposition or secondary waste material).In Fig. 1, above-mentioned forming element classification is briefly referred to as people, machine, material, method, ring, energy, gives up.
Specifically, the data after cleaning are established the data model of each production factors classification according to production factors classification
Mode are as follows: from each data after cleaning, extract data (the referred to as classification number for belonging to identical production factors classification
According to) and by the corresponding storage of categorical data, form the data model of the production factors classification.
For example, temporary layer gets following employee's tables of data 1 from human resource system, from manufacturing execution system
(manufacturing execution system, MES) gets following employee's tables of data 2.
Table 1: employee's tables of data 1:
| Name | Gender | Age |
| Zhang San | 0 | 30 |
| Li Si | 1 | 25 |
Table 2: employee's tables of data 1:
| Name | Gender | Responsible process number |
| Zhang San | Male | 1 |
| Li Si | Male | 2 |
After operation layer cleans this two employee's tables of data, detail layer is inquired from two tables of data to be belonged to
The field of production factors classification " people ": " name " and " gender ", therefore, detail layer extract two tables of data in " name " and
" gender " field merges in a tables of data, and deposit field relevant to " name " and " gender " in Tables 1 and 2 is corresponding
Storage forms table 3, i.e., establishes different field " age " and " responsible process number " in Tables 1 and 2 in a tables of data
Incidence relation, formed personnel's classification data model.
Table 3: the data model of production factors " people "
| Name | Gender | Age | Responsible process number |
| Zhang San | Male | 30 | 1 |
| Li Si | Male | 25 | 2 |
As can be seen that the data model established, by the corresponding storage of Data Integration in two incoherent tables of data originally
In a tables of data, i.e., according to production factors classification, the incidence relation between incoherent tables of data originally is established, also
It is to say, has got through incoherent each tables of data originally.
Further, detail layer is also used to the format of the data in unified data model, connects example, in two tables of data,
The numeric format of " gender " field is different, distinguishes men and women using 0 and 1 in a tables of data, and the Chinese is used in another tables of data
Word " male " and " female ".So the numerical value of " gender " field can also be unified for pre-set gender format: Chinese character by detail layer
After " male " and " female ", then will field relevant to " people ", merge in a tables of data.
4, summarize layer for using the data model of foundation obtaining data analysis result according to data analysis requirements.
Data analysis requirements can be the data analysis requirements of the user obtained from client, for example, user intentionally gets
Relation curve between the age of employee and the process that can be responsible for.
Optionally, some data analysis requirements can also independently be determined by summarizing layer: be summarized known to layer one in industrial flow
A little intrinsic data analysis requirements, and existing data model is combined, intrinsic number can be supported by analyzing data model
According to certain data analysis requirements in analysis demand.For example, as shown in fig. 1, can by with " molding " in Flow Manufacturing
Data relationship relevant etc. some process flows is as data analysis requirements, for example, what the links of " molding " technique used
Personnel amount obtains data analysis result using the data model of foundation.
It, can be preparatory using the specific algorithm that the data model of foundation calculates data analysis result according to data analysis requirements
It is stored in and summarizes in layer, summarize layer according to data analysis requirements and the data model of foundation and select corresponding algorithm.
5, application layer is used to receive the demand of user, by the demand feedback of user to summarizing layer, and will summarize what layer obtained
Data analysis result is sent to client.
That is, application layer is equivalent to an interface, for connecting client and summarizing layer, so that data warehouse can be with
Dock various clients.
In Fig. 1, the demand that application layer receives user includes: technique recommendation, cost optimization, parameter scores etc., will be needed anti-
It is fed to after summarizing layer, output summarizes tables of data of the layer according to the demand feedback of user.
Other than the demand of the user shown in Fig. 1, data warehouse shown in FIG. 1 can also provide following business and need
It asks:
Production monitoring alarm: the line of production control in real time is generated by the excellent parameter curve of model learning and does monitoring alarm.
Technological parameter is recommended: by the excellent technological parameter of model learning, recommending Optimizing Process Parameters combination.
Spare part loss prediction: by model learning spare part auxiliary material life cycle, full range prediction (from real time to offline).
Product yield prediction: by the excellent parameter curve of model real time contrast, this batch yield is predicted in real time.
Analysis on Fault Diagnosis: by model learning mechanical disorder tree, according to input fault show analysis failure root because.
Above each layer, can will treated that data store after handling data.
It can be seen that data warehouse shown in FIG. 1, Process-Oriented manufacturing industry, the data solved in Flow Manufacturing exist
If the problem of how cloud handles, is associated with and how to carry out data analysis.
Data warehouse shown in FIG. 1 can be abstracted to obtain structure shown in Fig. 2, comprising:
First obtains module, the second acquisition module and analysis module.
Wherein, for the first acquisition module for obtaining data analysis requirements, the data analysis requirements include number to be analyzed
According to type.Second, which obtains module, is used for based on the data type to be analyzed, from the industrial data pre-established at least
The data model of one production factors classification obtains associated data.Analysis module, for the described associated of acquisition
Data are analyzed, and obtained analysis result is sent to user.Wherein, the production factors classification refers to industrial production stream
The corresponding classification of each production factors needed for producing one production process of a product or operation in journey；The production factors class
Other data model is determined based on the data for belonging to the production factors classification extracted from the industrial data.
As it can be seen that the function that the first acquisition module is realized includes that application layer obtains the analysis demand of user and to summarize layer autonomous
Determine the function of data analysis requirements.Second acquisition module for realizing function include temporary layer, operation layer and detail layer
Function (combines for three layers and pre-establishes data model).Analysis module for realizing function include summarize layer obtain analysis result with
And application layer sends the function of analysis result.The specific implementation of the function of modules may refer to the corresponding implementation of Fig. 1
Example, which is not described herein again.
Below with an example, illustrate the process that data warehouse shown in Fig. 2 analyzes industrial data.
Assuming that rubber tyre manufacturing enterprise needs to check the product qualification rate report of the sizing material of daily plant produced, then Fig. 2
Shown in data warehouse, the process of output products qualification rate report is as shown in Figure 3, comprising the following steps:
S301: the second, which obtains module, obtains production batch table from execution system (MES) is manufactured, from quality control system
(QM) quality inspection detail list is obtained.
It wherein, include: the date of manufacture in production batch table, batch number, (vehicle is the work for carrying sizing material to vehicle number
Tool, a batch often produces more vehicle sizing materials), type of raw materials.
It include: batch number in quality inspection detail list, (vehicle is the tool for carrying sizing material to vehicle number, and a batch is often
Produce more vehicle sizing materials), for indicating whether qualified mark.
S302: the second acquisition module filters out dirty data or wrong data according to professional standard.
For example, regulation batch number was indicated using the time in professional standard, and such as: 20170707001, but practical hair
It include the data that format is XXXXX110QPX in the table that existing temporary layer obtains, it is determined that do not meet professional standard, then delete.
Further, in order to guarantee data delete preciseness, client can also be sent by data to be deleted, by
User is confirmed as wrong data, and after receiving the wrong data confirmation message of client feedback, data processing module is deleted again
Data.
S303: the second acquisition module finds data relevant to " machine " from production batch table and quality inspection detail list
" vehicle number ", from production batch table and quality inspection detail list extract " vehicle number " field, and with " vehicle number " field for be associated with according to
According to establishing the corresponding relationship of other fields in production batch table and quality inspection detail list, form a tables of data, that is, the number established
According to model.
It include: vehicle number, date of manufacture, batch number, type of raw materials and for indicating whether in the tables of data of formation
Qualified attribute field.
Wherein, S301-S303 is the second process for obtaining the pre-generated data model of module.
S304: the first obtains the data analysis requirements that module receives user from client: the sizing material of daily plant produced
Product qualification rate report (data type i.e. to be analyzed), and data analysis module is sent by the demand.
S305: data analysis requirements of the analysis module according to user are grouped according to the date of manufacture, respectively to qualified train number and
Unqualified train number face train number number counts, and obtains new polymerization computational chart, including date of manufacture, qualified vehicle vehicle number is unqualified
Vehicle digital section.Qualified vehicle number is obtained into a day qualification rate report, including date of manufacture and conjunction multiplied by 100% divided by unqualified vehicle number again
Two fields of lattice rate.
S306: day qualification rate report is issued client by analysis module.
It can be seen that in the present embodiment from process shown in Fig. 3, give a kind of data analysis side for industrial data
Method can provide a user the big data analysis of industrial data as a result, meeting the needs of users.
Temporary layer, operation layer and detail layer shown in FIG. 1 can be abstracted as the processing unit of industrial data shown in Fig. 4,
It include: to obtain module (corresponding temporary layer and run layer), abstraction module (partial function of corresponding detail layer) and to establish module (right
Answer the partial function of detail layer).
Specifically, obtaining module for obtaining industrial data to be processed.Obtain what the data source exported from data source
Industrial data, and it is based on preset filtering rule, processing is filtered to the industrial data, is obtained described to be processed
Industrial data.Wherein, filtering rule can be arranged according to the professional standard of industry.
Abstraction module belongs to the data of each production factors classification for extracting from the industrial data, wherein described each
Production factors classification refers to that respectively production is wanted needed for producing one production process of a product or operation in industrial process stream
The corresponding classification of element.
Establish module for based on extract obtain described in belong to the data of each production factors classification, establish each production factors
The data model of classification, so that the data model based on foundation analyzes analysis demand required for user.Into one
Step, it establishes module and is also used to: after the data model for establishing each production factors classification, by each production factors class
The uniform format for belonging to the data of the same production factors classification in other data model is that preset format is (such as shown in FIG. 1
Gender format in embodiment is " male " and " female ").
The processing unit of industrial data shown in Fig. 4 can establish data model based on industrial data, be industrial data
Big data analysis lays the foundation.
The embodiment of the present application also discloses a kind of processing unit of industrial data, including processor and memory.
Wherein, memory is for storing application program.Processor, for running the application program, to realize following function
Can: obtain industrial data to be processed；The data for belonging to multiple production factors classifications are extracted from the industrial data, wherein
The production factors classification refers to production needed for one production process of one product of production or operation in industrial process stream
The corresponding classification of element；Based on extract obtain described in belong to the data of multiple production factors classifications, establish the multiple production
The data model of feature category, so that the data model based on foundation analyzes analysis demand required for user.
The specific implementation of above functions may refer to above method embodiment, and which is not described herein again.
The embodiment of the present application also discloses a kind of data warehouse, including processor and memory.
Wherein, memory is for storing application program.Processor, for running the application program, to realize following function
Can: data analysis requirements are obtained, the data analysis requirements include data type to be analyzed；Based on the data to be analyzed
Type obtains associated data from the data model of the multiple production factors classifications pre-established；To the phase of acquisition
Associated data are analyzed, and obtained analysis result is supplied to user；Wherein, the production factors classification refers to industry
The corresponding classification of production factors needed for producing one production process of a product or operation in production procedure；The production is wanted
The data model of plain classification is determined based on the data for belonging to production factors classification extracted from industrial data.
The specific implementation of above functions may refer to above method embodiment, and which is not described herein again.
Another data warehouse disclosed in the embodiment of the present application, comprising: including processor and memory.
Wherein, memory is for storing application program.Processor, for running the application program, to realize in Fig. 1
Application layer (module), the function of summarizing layer (module) and detail layer (module) optionally can also realize operation layer (module)
Function.
If function described in the embodiment of the present application method is realized in the form of SFU software functional unit and as independent production
Product when selling or using, can store in a storage medium readable by a compute device.Based on this understanding, the application is real
The part for applying a part that contributes to existing technology or the technical solution can be embodied in the form of software products,
The software product is stored in a storage medium, including some instructions are used so that a calculating equipment (can be personal meter
Calculation machine, server, mobile computing device or network equipment etc.) execute each embodiment the method for the application whole or portion
Step by step.And storage medium above-mentioned include: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), with
Machine accesses various Jie that can store program code such as memory (RAM, Random Access Memory), magnetic or disk
Matter.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with it is other
The difference of embodiment, same or similar part may refer to each other between each embodiment.
The foregoing description of the disclosed embodiments makes professional and technical personnel in the field can be realized or use the application.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the application.Therefore, the application
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (15)
1. a kind of processing method of industrial data characterized by comprising
Obtain industrial data to be processed；
The data for belonging to multiple production factors classifications are extracted from the industrial data, wherein the production factors classification refers to
The corresponding classification of production factors needed for producing one production process of a product or operation in industrial process stream；
Based on extract obtain described in belong to the data of multiple production factors classifications, establish the data mould of multiple production factors classifications
Type, so that the data model based on foundation analyzes analysis demand required for user.
2. the method according to claim 1, wherein producing a product or fortune in the industrial process stream
The corresponding classification of production factors needed for one production process of row includes:
Produce in one production process of a product or operation, the people that is related to, machine, material, method, ring, energy, give up in one or more
It is a.
3. obtaining industrial data to be processed the method according to claim 1, wherein described and including:
The industrial data that the data source exports is obtained from data source；
Filtering rule based on setting is filtered processing to the industrial data, obtains the industrial data to be processed.
4. method according to claim 1-3, which is characterized in that establish multiple production factors classifications described
Before data model, further includes:
The data for belonging to same production factors classification are unified for the data of preset format.
5. a kind of industrial data analysis method characterized by comprising
Data analysis requirements are obtained, the data analysis requirements include data type to be analyzed；
Based on the data type to be analyzed, obtained from the data model of the multiple production factors classifications pre-established related
The data of connection；
The associated data of acquisition are analyzed, and obtained analysis result is supplied to user；
Wherein, the production factors classification, which refers to, produces one production process institute of a product or operation in industrial process stream
The corresponding classification of production factors needed；The data model of the production factors classification belongs to life based on what is extracted from industrial data
The data for producing feature category determine.
6. according to the method described in claim 5, it is characterized in that, the acquisition data analysis requirements include:
Receive the data analysis requirements of user's input；
Alternatively, being needed according to the data model for pre-establishing the production factors classification and the corresponding data analysis of industry
It asks, determines the corresponding data analysis requirements of the data model.
7. a kind of processing unit of industrial data characterized by comprising
Module is obtained, for obtaining industrial data to be processed；
Abstraction module, for extracting the data for belonging to multiple production factors classifications from the industrial data, wherein the production
Feature category refers to that production factors needed for one production process of one product of production or operation are corresponding in industrial process stream
Classification；
Establish module, for based on extract obtain described in belong to the data of multiple production factors classifications, establish multiple productions and want
The data model of plain classification, so that the data model based on foundation analyzes analysis demand required for user.
8. device according to claim 7, which is characterized in that produce a product or fortune in the industrial process stream
The corresponding classification of production factors needed for one production process of row includes:
Produce a product or operation one production process, the people being related to, machine, material, method, ring, energy, give up in one or more
It is a.
9. a kind of data warehouse characterized by comprising
First obtains module, and for obtaining data analysis requirements, the data analysis requirements include data type to be analyzed；
Second obtains module, for based on the data type to be analyzed, from the multiple production factors classifications pre-established
Associated data are obtained in data model；
Obtained analysis result for analyzing the associated data of acquisition, and is supplied to use by analysis module
Family；
Wherein, the production factors classification, which refers to, produces one production process institute of a product or operation in industrial process stream
The corresponding classification of production factors needed；The data model of the production factors classification belongs to life based on what is extracted from industrial data
The data for producing feature category determine.
10. device according to claim 9, which is characterized in that the first acquisition module is specifically used for:
The data analysis requirements for receiving user's input, alternatively, pre-establishing the production factors classification according to described
Data model and the corresponding data analysis requirements of industry, determine the corresponding data analysis requirements of the data model.
11. a kind of data warehouse characterized by comprising application module, summarizing module and detail module；Wherein,
The detail module is used for the production factors classification according to industrial flow, is belonged to using what is extracted from the industrial data
The data of the production factors classification establish the data model of the production factors classification；Wherein, the production factors classification is
Refer to the corresponding classification of production factors needed for producing one production process of a product or operation in industrial process stream；
The application module is for obtaining data analysis requirements；
The summarizing module is used to receive the data analysis requirements of the application module feedback, and uses the detail module
The data model of foundation, obtains data analysis result.
12. data warehouse according to claim 11, which is characterized in that further include:
Module is runed, for filtering the data for being unsatisfactory for the industrial standard in the industrial data according to industrial standard.
13. a kind of processing unit of industrial data characterized by comprising
Memory, for storing application program；
Processor, for running the application program, to implement function such as: obtaining industrial data to be processed；From the work
The data for belonging to multiple production factors classifications are extracted in industry data, wherein the production factors classification refers to industrial process stream
The corresponding classification of production factors needed for one production process of one product of middle production or operation；Based on extract obtain described in
The data for belonging to multiple production factors classifications establish the data model of the multiple production factors classification, so as to based on foundation
The data model analyzes analysis demand required for user.
14. a kind of data warehouse characterized by comprising
Memory, for storing application program；
Processor, for running the application program, to implement function such as: obtaining data analysis requirements, the data analysis
Demand includes data type to be analyzed；Based on the data type to be analyzed, from the multiple production factors classes pre-established
Associated data are obtained in other data model；Point that the associated data of acquisition are analyzed, and will be obtained
Analysis result is supplied to user；Wherein, the production factors classification, which refers to, produces a product or operation in industrial process stream
The corresponding classification of production factors needed for one production process；The data model of the production factors classification is based on from industrial data
The data for belonging to production factors classification of middle extraction determine.
15. a kind of data warehouse characterized by comprising
Memory, for storing application program；
Processor, for running the application program, to implement function such as: according to the production factors classification of industrial flow, making
With the data for belonging to the production factors classification extracted from the industrial data, the data of the production factors classification are established
Model；Wherein, the production factors classification, which refers to, produces one production process of a product or operation in industrial process stream
The corresponding classification of required production factors；Obtain data analysis requirements；The data analysis requirements are received, and use the data
Model obtains data analysis result.
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